Written by: Tiger Research
Compiled by: AididiaoJP, Foresight News
A fear of missing out is sweeping through the crypto industry. From exchanges to security firms, various organizations are launching AI-powered services. This article will explore the reasons why these companies are choosing to make this move at this time.
Key Points
- Crypto companies covering areas such as exchanges, security, payments, and research are simultaneously launching artificial intelligence-related services.
- Unlike previous cycles, this round is led by established companies like Coinbase and Binance, which already possess mature profit models. Artificial intelligence has evolved from a hype concept into an essential operational capability.
- The motivations for adopting artificial intelligence vary across industries: exchanges aim to reduce user churn; security companies focus on filling audit blind spots; and payment infrastructure targets the emerging intelligent agent economy.
- There is a gap between feature launch and actual application. Fear of missing out on artificial intelligence and competitive pressure are driving companies to accelerate their deployments at a pace that exceeds actual needs.
- Genuine demand and competitive anxiety are driving this wave. The core issue is how to distinguish between applications that truly create value and mere OEM (Original Equipment Manufacturer) activities that are merely superficial.
Crypto companies are launching AI services one after another.
Artificial intelligence is currently the most watched field in the global market. General-purpose tools such as ChatGPT and Claude have been integrated into daily life, while platforms such as OpenClaw have further lowered the technical barriers to building intelligent agents.
While the crypto industry has been somewhat slow to react to this wave, it is now accelerating the integration of artificial intelligence capabilities across various vertical sectors.
What specific AI services have these companies launched? And what motivated them to enter this field?
How crypto companies can apply artificial intelligence
field of study
Source: Surf AI
Cryptographic research suffers from structural problems: on-chain data, market sentiment, and key metrics are scattered across different platforms, making verification difficult. General-purpose artificial intelligence often provides inaccurate answers when dealing with cryptographic-related issues.
In response to this situation, projects like Surf have launched dedicated AI research tools for the crypto space, integrating fragmented data sources. Among all AI applications in the crypto field, research tools have the lowest barrier to entry for ordinary users, requiring no programming or trading experience to use.
Trading Field
Source: Bitget
The exchange is at the forefront of artificial intelligence applications.
The approaches taken by different exchanges vary. Some directly open their proprietary trading data to users; others allow users to issue commands to an AI agent in natural language, which then completes the analysis and execution in one step.
Exchanges have been offering API services for many years. The current change lies in the addition of an interaction layer: through interfaces such as MCP and AI Skills, non-developers can also use artificial intelligence agents to access exchange functions. Tools that were originally limited to developers can now be operated using natural language.
This change aligns with the evolving user base. A growing number of users without programming backgrounds are leveraging AI agents to build automated trading strategies. Users simply describe their strategy, and the AI agent can then build and run the algorithm.
For exchanges, this trend presents both opportunities and challenges. As the user base driven by artificial intelligence continues to grow, their stickiness to a single platform decreases because intelligent agents can flexibly execute trades across different exchanges. The core motivation for exchanges to actively deploy artificial intelligence is to quickly attract users and increase their activity within the platform.
Unlike information retrieval apps, transactions involve real asset management, demanding higher levels of judgment and accountability. However, as the barriers to entry gradually decrease, this field is also opening its doors to ordinary users.
Security and Auditing
Source: Certik
Smart contract auditing traditionally relies on manual line-by-line code review, a slow and costly process with inconsistent audit quality depending on the executor. Currently, artificial intelligence (AI) has been incorporated into the workflow: AI first performs code scanning, followed by targeted in-depth review by auditors. This approach improves efficiency and coverage without replacing auditors.
CertiK is a representative company in this field. The company has previously faced scrutiny due to security incidents that occurred after some of its audit projects. However, these incidents mostly occurred outside the scope of the audits—the audits only covered code at specific points in time and did not include ongoing monitoring.
CertiK addresses this shortcoming by leveraging artificial intelligence. It introduces a real-time monitoring mechanism after audits are completed and displays the results publicly through a dashboard. Since this expanded monitoring capability is driven by AI, it requires minimal human intervention, thus benefiting both CertiK and its audited entities.
In the security field, the application of artificial intelligence is not intended to disrupt existing services, but rather to expand the boundaries of human work: improving the accuracy of the audit process and filling monitoring blind spots in the post-audit stages. For blockchain security companies, artificial intelligence is not a new business direction, but a tool to solve pain points in existing businesses.
Payment infrastructure sector
Source: Coinbase
For AI agents to participate in economic activities, they must have available payment channels, such as paying for APIs, purchasing data, and procuring services from other agents. For these agents, the most suitable payment method is an on-chain wallet paired with a stablecoin.
Currently, there are two main models. The first is a general protocol that embeds payment functionality into HTTP requests, allowing the agent to complete on-chain settlement synchronously when calling the payment API. The second is a payment plugin for agents, where the agent only performs payment operations within the limits and permissions preset by humans.
Payment infrastructure is the area most closely related to stablecoins. However, since the payment entities are AI agents rather than natural persons, a fully mature operating model does not yet exist.
Source: Circle
Circle, the issuer of the stablecoin USDC, has also attracted market attention. The company has released a proposal to integrate its Gateway payment infrastructure with the x402 protocol and has invited developers and researchers to participate in the review and co-construction.
This field is not yet mature, but the market has already begun to price in related expectations. One of the key drivers of Circle's stock price increase is the narrative surrounding AI-powered payments. Compared to the aforementioned areas, the realization of payment infrastructure will take longer, but it has already established itself as one of the most important macro themes in the current market.
Why are crypto companies entering the field of artificial intelligence at this time?
When ChatGPT was launched in November 2022, both the artificial intelligence and crypto industries were still in their infancy. While the AI model demonstrated some capabilities, it was not yet able to reliably complete tasks; the crypto industry was facing a severe crisis of trust due to the FTX crash.
Since then, artificial intelligence technology has made significant progress. Over the past year, the capabilities of all mainstream models have improved substantially, and their practicality has increased markedly. In contrast, the crypto industry during the same period has largely remained at the stage of "borrowing" the concept of artificial intelligence, manifested in AI-themed meme coins, AI agents lacking practical functionality, and marketing-driven promotional rhetoric. Decentralized AI infrastructure projects continue to emerge, but their product quality lags significantly behind that of similar native AI services.
Currently, the gap continues to widen. In the field of artificial intelligence, the maturity of infrastructures such as MCP (which allows intelligent agents to directly call external tools) and OpenClaw (which supports no-code construction of intelligent agents) has brought the era of intelligent agents from concept to reality. Cryptocurrency companies are only now beginning to follow suit in a substantial way.
The key to this shift lies in the different actors. The leaders are no longer emerging projects using the AI concept for branding, but rather established companies with stable revenue models—Coinbase, Binance, Bitget, and others. These companies have no incentive to use AI services as a marketing gimmick. The core driver of their actions is not current profits, but rather the anxiety of falling behind industry development—a fear of missing out.
Source: FORTUNE
This sense of urgency is evident in the actions of Coinbase CEO Brian Armstrong. He demanded that all engineers complete hands-on training on AI coding tools within a week, and those who failed to meet the requirements were dismissed.
However, maintaining prudent judgment is equally essential. Take trading automation as an example: AI agents can provide price quotes and strategy suggestions, but how many users are actually willing to entrust their funds to these agents for live trading? Has the x402 protocol entered the practical application stage?
Overall, the crypto industry's foray into artificial intelligence is not a pursuit of short-term trends. As the outline of the AI era gradually becomes clearer, companies are accelerating their efforts to solidify their industry position. While there is still a gap between feature launch and actual application, the very identity of the actors involved is of significant importance.
The artificial intelligence industry can be likened to a swimming pool that is being filled. Many early entrants were merely pretending to swim. Current entrants, however, are seasoned players with deep experience. How high the water level will rise, and whether the pool will expand into an ocean, remains to be seen. But one thing is certain: the crypto industry will not be marginalized in this wave.
